Adaptive mixed-norm filtering algorithm based on SαSG noise model

Zha, Daifeng; Qiu, Tianshuang

doi:10.1016/j.dsp.2006.01.002

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…In the field of signal processing, the noise is mostly described by Gaussian distribution model, which has an excellent effect on two-order statistics of normal distribution [1]. When it comes to the frequency-domain analysis of noise, the existing theoretical methods include spectral feature analysis, time-frequency analysis, spectrum estimation, colored noise whitening, spatial spectrum estimation, frequency estimation, harmonic estimation and spectral line restoration.…”

Section: Introductionmentioning

confidence: 99%

A Fractional Lower-order Bi-spectrum Estimation Method Based on Autoregressive Model

Yang¹

2019

I2M

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The traditional fractional lower-order (FLO) spectrum estimation method cannot observe a sufficient volume of data, leading to a large variance of estimation results. To solve the problem, this paper puts forward a FLO bi-spectrum estimation method based on autoregressive (AR) model, and gives new definitions for FLO three order cumulant. The author discussed the determination of AR model parameters, and introduced how to implement the bi-spectrum estimation method based on AR model. Then, a series of tests were performed to verify the correctness of our method. The results show that our method outperformed the traditional approaches in suppressing FLO noise and identifying relevant information of signals.

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Section: Introductionmentioning

confidence: 99%